ON THE ERROR OF APPROXIMATION BY RBF NEURAL NETWORKS WITH TWO HIDDEN NODES
DOI10.30546/2409-4994.47.2.226zbMath1498.41016OpenAlexW4200228007MaRDI QIDQ5863188
Ibrahim K. Maharov, Arzu M-B. Babayev
Publication date: 11 March 2022
Published in: Proceedings of the Institute of Mathematics and Mechanics, National Academy of Sciences of Azerbaijan (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.30546/2409-4994.47.2.226
Artificial neural networks and deep learning (68T07) Neural networks for/in biological studies, artificial life and related topics (92B20) Approximation by other special function classes (41A30) Numerical radial basis function approximation (65D12)
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Linear and nonlinear approximation of spherical radial basis function networks
- Universal approximation by radial basis function networks of Delsarte translates
- Interpolation on lines by ridge functions
- A note on the representation of continuous functions by linear superpositions
- Approximation by a sum of two algebras. The lightning bolt principle
- Reinforcement radial basis function neural networks with an adaptive annealing learning algorithm
- Interpolation by Ridge functions
- A note on the equioscillation theorem for best ridge function approximation
- On the error of approximation by ridge functions with two fixed directions
- On the approximation of a function of several variables by the sum of functions of fewer variables
- Uniform approximation by real functions
- Approximation by Solutions of the Planar Wave Equation
- On the proximinality of ridge functions
- Approximation by sums of ridge functions with fixed directions
- A CHEBYSHEV THEOREM FOR THE APPROXIMATION OF A FUNCTION OF TWO VARIABLES BY SUMS OF THE TYPE ϕ(x) + ψ(y)
This page was built for publication: ON THE ERROR OF APPROXIMATION BY RBF NEURAL NETWORKS WITH TWO HIDDEN NODES
